AI BASED IMAGE CLASSIFICATION NUTRITION ESTIMATOR FOR WEIGHT ANALYSIS

Abstract

In recent years, artificial intelligence has significantly transformed healthcare and nutrition analysis by enabling automated and accurate dietary assessments. This paper proposes an AI-Based Image Classification Nutrition Estimator for Weight Analysis that uses deep learning techniques to identify food items from images and estimate their nutritional content, including calories, proteins, carbohydrates, and fats. The system integrates convolutional neural networks (CNNs) for image classification and a nutritional database for mapping food items to their nutritional values. The primary objective is to help users monitor their diet and manage body weight effectively without manual tracking. Traditional nutrition tracking methods are timeconsuming and error-prone, whereas the proposed system provides real-time analysis through simple image input. The model is trained on a diverse dataset of food images to improve accuracy across different cuisines and food presentations. This system can be used in fitness applications, hospitals, diet planning, and personal health monitoring. By combining computer vision and machine learning, the proposed solution enhances accessibility, efficiency, and precision in dietary assessment and weight management.